Learning novel concepts, remembering previous knowledge, and adapting it to
future tasks occur simultaneously throughout a human's lifetime. To model such
comprehensive abilities, continual zero-shot learning (CZSL) has recently been
introduced. However, most existing methods overused unseen semantic information
that may not be continually accessible in realistic settings. In this paper, we
address the challenge of continual zero-shot learning where unseen information
is not provided during training, by leveraging generative modeling. The heart
of the generative-based methods is to learn quality representations from seen
classes to improve the generative understanding of the unseen visual space.
Motivated by this, we introduce generalization-bound tools and provide the
first theoretical explanation for the benefits of generative modeling to CZSL
tasks. Guided by the theoretical analysis, we then propose our learning
algorithm that employs a novel semantically guided Generative Random Walk (GRW)
loss. The GRW loss augments the training by continually encouraging the model
to generate realistic and characterized samples to represent the unseen space.
Our algorithm achieves state-of-the-art performance on AWA1, AWA2, CUB, and SUN
datasets, surpassing existing CZSL methods by 3-7\%. The code has been made
available here https://github.com/wx-zhang/IGCZSL

通过利用生成建模，我们提出了一个学习算法，在没有给定未知信息的情况下持续进行零样本学习，并在 AWA1、AWA2、CUB 和 SUN 数据集上实现了超出现有连续零样本学习方法的 3-7% 的最新性能。

通过语义引导的生成随机游走实现持续的零样本学习

Continual Zero-Shot Learning through Semantically Guided Generative  Random Walks

Continual zero-shot learning(CZSL) is a new domain to classify objects
sequentially the model has not seen during training. It is more suitable than
zero-shot and continual learning approaches in real-case scenarios when data
may come continually with only attributes for a few classes and attributes and
features for other classes. Continual learning(CL) suffers from catastrophic
forgetting, and zero-shot learning(ZSL) models cannot classify objects like
state-of-the-art supervised classifiers due to lack of actual data(or features)
during training. This paper proposes a novel continual zero-shot learning
(DVGR-CZSL) model that grows in size with each task and uses generative replay
to update itself with previously learned classes to avoid forgetting. We
demonstrate our hybrid model(DVGR-CZSL) outperforms the baselines and is
effective on several datasets, i.e., CUB, AWA1, AWA2, and aPY. We show our
method is superior in task sequentially learning with ZSL(Zero-Shot Learning).
We also discuss our results on the SUN dataset.

本文提出了一种新的继续零样本学习模型 (DVGR-CZSL)，通过生成式回放来防止灾难性遗忘，该模型与先前学习过的类一起更新。实验证明，这种混合模型在几个数据集中优于基线，能够有效地进行任务顺序学习，并且在零样本学习中具有优越性。

基于生成回放的动态 VAE 模型实现持续零样本学习

Dynamic VAEs with Generative Replay for Continual Zero-shot Learning

Most of the existing artificial neural networks(ANNs) fail to learn
continually due to catastrophic forgetting, while humans can do the same by
maintaining previous tasks' performances. Although storing all the previous
data can alleviate the problem, it takes a large memory, infeasible in
real-world utilization. We propose a continual zero-shot learning model(A-CZSL)
that is more suitable in real-case scenarios to address the issue that can
learn sequentially and distinguish classes the model has not seen during
training. Further, to enhance the reliability, we develop A-CZSL for a single
head continual learning setting where task identity is revealed during the
training process but not during the testing. We present a hybrid network that
consists of a shared VAE module to hold information of all tasks and
task-specific private VAE modules for each task. The model's size grows with
each task to prevent catastrophic forgetting of task-specific skills, and it
includes a replay approach to preserve shared skills. We demonstrate our hybrid
model outperforms the baselines and is effective on several datasets, i.e.,
CUB, AWA1, AWA2, and aPY. We show our method is superior in class sequentially
learning with ZSL(Zero-Shot Learning) and GZSL(Generalized Zero-Shot Learning).

文章提出了一种基于 VAE 模块的连续零样本学习模型 (A-CZSL)，用于解决人工神经网络与人类区别在于传统模型很难取得不同阶段任务良好的表现，模型大小在任务逐步增加的同时增长以防遗忘，并展示其在零样本学习和广义零样本学习上的优越性能。